62
Ind. Eng. Chem. Process Des. Dev., Vol. 17, No. 1, 1978
An Experimental Application of Time Delay Compensation Techniques to Distillation Column Control Christoph Meyer, Dale E. Seborg,+l and Reg K. Wood Department of Chemical Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2G6
Two time delay compensation techniques, the Smith predictor and the “analytical predictor”, are used to control top composition of a pilot scale distillation column. In each control scheme the predictive model consists of a first-order plus time delay transfer function developed empirically from open-loop data. Experimental and simulation studies are used to evaluate the predictive control schemes and to compare their performance with conventional PI control.
Introduction Time delays are a common occurrence in the process industries due to recycle loops, distance-velocity lags in fluid flow, and the “dead time” inherent in many composition analyses. The detrimental effects of time delays on system stability and control performance are well known, and consequently there is considerable incentive for the development of special control techniques which compensate for time delays. The technique that has received the most attention, the Smith predictor (Smith, 1957,1959),is well known in industrial and academic circles; a second technique, the “analytical predictor”, was developed by Moore (Moore, 1969; Moore et al., 1970) in 1969 but has received relatively little attention including only a single experimental study (Doss and Moore, 1973; Doss, 1974). The purpose of this investigation is to provide additional practical experience with the analytical and Smith predictors by applying these techniques to a pilot scale distillation column. Distillation columns are a promising application area for time delay compensation techniques due to their widespread use in the process industries (Rademaker et al., 1975; Shinskey, 1977) and the presence of large time delays. In this paper the Smith predictor, analytical predictor, and a conventional PI controller are used to control the top composition of a methanol-water column. The application of these techniques to bottoms composition control of the same column is the subject of a related investigation (Meyer, 1977; Meyer et al., 1977). Predictor Control Techniques A block diagram of a conventional feedback control system is shown in Figure 1with controlled variable C, manipulated variable U,set point (or reference input) Rset,and disturbance (or load variable) D. In most process control applications the primary design objective is the specification of controller transfer function, C,(s), so that satisfactory regulatory and servo control is achieved. If time delays TI and Tz are large, this objective may be difficult to attain since the controller gain must be reduced to maintain stability, and long response times will result. Smith Predictor (SP). Of the special control techniques that have been proposed for processes with time delays, the Smith predictor (Smith, 1957; 1959) has received the most attention. Several experimental investigations (Doss and Moore, 1973; Doss, 1974; Buckley, 1960; Lupfer and Oglesby, 1961, 1962; Alevisakis and Seborg, 1974; Prasad and KrishAuthor to whom correspondence should be addressed a t the Department of Chemical and Nuclear Engineering, University of California, Santa Barbara, Calif. 93106.
naswamy, 1975) and simulation studies (Nielsen, 1969, Smith and Groves, 1973; Meyer et al., 1976) have demonstrated that the Smith predictor can result in significant improvements over conventional PID control. However, Nielsen (1969) and Meyer et al. (1976) have shown that such improvement does not always occur for regulatory control. The sensitivity of the SP method to modeling errors has dso received considerable attention (Buckley, 1960; Eisenberg, 1967; Garland and Marshall, 1974, 1975; Marshall, 1974). In recent years the method has been extended to sampled-data systems (Doss and Moore, 1973; Marshall, 1974; Gray and Hunt, 1971a,b; Alevisakis and Seborg, 1973) and multivariable control problems (Alevisakis and Seborg, 1973,1974; Mee, 1973). A block diagram of the discrete form of the Smith predictor, including a zero order hold (ZOH), is shown in Figure 2 with ck, uk, and dk denoting the values of variables c, u, and D , respectively, at the k t h sampling instant. The feedback loop around the digital controller contains a block whose output represents the difference between two model outputs: the response of a system without time delays, c’h, minus the response of a system which contains time delays, C”k. For the ideal case where the process models are exact, then d ’ k = Ck and ek = rk - C’k. Thus the control action is based on c‘k, the response of the undelayed model, and time delays TI and Tz do not appear in the characteristic equation. This means that a larger controller gain can be used thereby reducing the response time of the closed-loop system. In practical situations the presence of modeling errors may prevent some of this improvement from being realized. In this application the process model is chosen to be a first-order system plus time delay dc dt
+ c ( t ) = K,u(t - T ) (1) where T , the total time delay, is T = 2’1 + T2 or T = ( N + rP -
P)T,, T, is the sampling period, N is a nonnegative integer, and 0 < 0< 1. Since a sampler and zero-order hold are being used, the manipulated variable is constant between sampling instants. Integrating eq 1over the time interval between the ( k - 1) and kth sampling instants and denoting the model response by c”k gives (Meyer, 1977) c”k = BC”k-1
+ BK,
where B exp[-T,/r,] and E exp[-@T,/r,]. If no time delays are present (i.e., T = 0), then the corresponding expression for the undelayed model is given by
0019-7882/78/1117-0062$01.00/0 0 1978 American Chemical Society
Ind. Eng. Chem. Process Des. Dev., Vol. 17, No. 1, 1978
63
following expression for t k + S from the analytical solution to eq 1 ?k+e = AEk+N+p
K p ( 1 - A)Uk
(7)
where C^k+N+p = EBNCk
+ B N K p ( l - E)Uk-N-] N
+ K p ( l - B ) i = 1 Bi-lUk-i
Figure 1. Conventional feedback control system.
(8)
and A exp [-Ts/2~p].The predicted output is then used in a proportional control algorithm of the form uk = KCbk
- c*k+8)
where K , is the controller gain and point”
Uk
H(5) 6 2‘
Figure 3. Analytical predictor (AP) control system.
+ BC’k-1
(3)
From Figure 2 it follows that the error signal ek can be expressed as ek = rk
- ck - (c‘k - C ” k )
where K , is the controller gain and 71 is the reset time. Analytical Predictor (AP). An alternative approach for time-delay compensation in feedback control systems, the “analytical predictor”, has been developed by Moore et al. (1969, 1970). In their approach a process model is used to predict the future output from current measurements and the predicted value is then sent to the controller. Simulation studies by Moore et al. (1969,1970)have shown that the analytical predictor can result in better performance than a P I controller, and it performed well despite large modeling errors. In the only experimental application to date, Doss and Moore (1973)applied the AP method to a stirred tank heating system. Their comparisons of the AP, SP, and P I control indicated that both predictors performed better than the P I controller. They also concluded that the AP was less sensitive than the SP to controller tuning. A block diagram illustrating the analytical predictor control scheme is shown in Figure 3 with c^k+d denoting the predicted output over a time interval, BT,, where T 8 = - 0.5 = N p 0.5 (6) TS Thus t k f e is a prediction of the future output over a time interval of ( N + p + 0.5)TSwhere ( N PIT, is the total system time delay and 0.5TSis the well-known correction for the effect of the zero-order hold. Moore et al. (1969,1970)derived the
+ + +
+ dk = K c ( p k - t k + e )
(11)
where dk denotes the measured value of the disturbance at the k t h sampling instant. Note that in eq 1 1 , dk has been added to uk because the block diagram in Figure 3 indicates that dk and U k affect ck in the same manner. For the more usual situation where the disturbance cannot be measured, an estimated disturbance d k can be used in place of dk to yield eq 12 uk
(4)
For a conventional P I digital control algorithm
+
is a “calibrated set
Set point calibration is required to eliminate the offset (Le., steady-state error) that occurs after step changes in set point when proportional control is used. Moore et al. (1969,1970)have also proposed a second control algorithm to compensate for the unknown disturbances which inevitably occur in process control systems. If the disturbance can be measured, then eq 9 can be modified to give
Figure 2. Smith predictor (SP) control system.
C’k = K p ( l -B)Uk-1
Pk
(9)
+ d k = Kdpk - t k + e )
(12)
By assuming that the disturbance is a step input of unknown magnitude, Moore et al. (1969, 1970) derived the following expression for a k dk
= &-I
+ KrTs(Ck - t k )
(13)
where c^k is the predicted value of ck made a t time, k T , - 0. Although an expression for K I can be derived in terms of B and E, K I is used as a tuning parameter in the AP control scheme. The estimated disturbance, &, can alsa be incorporated into eq 7 and 8 in order to provide a better prediction of the future output tk+B = Aek+N+o 4- K p ( 1 - A)(Uk
dk)
(14)
where ek+N+p
= EBNCk
+ BNKp(l- E ) ( U k - N - i -k d k ) N + K p ( 1 - B ) 1 = 1 Bi-l(Uk-i + d k )
(15)
By combining eq 12-15, the following expression for the AP control law is obtained (16)
Note that the AP control algorithm in eq 13,15, and 16 contains several parameters which can be adjusted. In previous studies by Moore et al. (1969, 1970) and Doss and Moore (1973, 1974), K , and K I were considered to be control parameters which could be tuned on-line while A and K p were
64
Ind. Eng. Chem. Process Des. Dev., Vol. 17, No. 1, 1978
__-_SIMULATION
COOLING WATER
-EXPERIMENTAL
_ _ _ -SIMULATION _
0
98.0r
~
EXPERIMENTAI
COMPUTER
,
CODE CR - COMPOSITION RECORDER
TIME (min)
FRC- FLOW RECORDERICONTROLLER LC - LEVEL CONTROLLER STEAM
t BOTTOM PRODUCT
Figure 4. Schematic diagram of the distillation column.
Figure 5. Open loop responses for step changes in reflux (top) and feed flow (bottom). of the process to be controlled must be available. Although the dynamics of the distillation column are inherently nonlinear, for operation near a nominal steady state the dynamics can be adequately approximated by a first-order plus time delay transfer function model (Pacey, 1973; Wood and Berry, 1973). For the operating conditions given in Table I, an appropriate transfer function model relating the effect of reflux flow R and feed flow rate F on top composition CD is
Table I. Steady-State Operating Conditions Feed flow rate Reflux flow rate Steam flow rate Feed composition Top composition Bottom composition Distillate flow rate Bottoms flow rate
18 gls 16 gls 14 g/s 50 wt % methanol 97% methanol 5% methanol 9.3 gls 8.7 g/s
considered to be constant model parameters. This same approach was adopted in the present investigation. It should also be noted that inclusion of the disturbance estimate, d k , in eq 16 provides a form of integral action since the estimated disturbance will change with time until Ck = Ek (cf. eq 13).
The parameter values in this transfer function model, particularly the gains of 1.0 and 0.167, differ significantly from those reported by Wood and Berry (1973) due to the change in operating conditions. In eq 17 the time constants and time delays are expressed in units of seconds. A comparison of simulated and experimental response data for open loop operation is shown in Figure 5. Clearly the process models represent only a very approximate characterization of the dynamic behavior of the column due to the strong nonlinearities that are evident. Since the top composition is very insensitive to an increase in feed flow rate, experimental testing of the control algorithms was performed for set point changes and a decrease in feed flow rate.
Equipment Description. The experimental equipment consists of a pilot scale methanol-water distillation column which is interfaced with an IBM 1800 data acquisition and control computer and is shown schematically in Figure 4. The 22.5-cm diameter column contains eight bubble cap trays on a 30.48 cm spacing. Each of the trays contains four bubble caps. Top composition is measured on a continuous basis by means of a capacitance cell located a t the base of the total condenser. Bottoms composition is measured with a HP-5720A gas chromatograph using an in-line liquid sampling system. Standard data acquisition of temperatures, flow rates, and pressures is done at one-second intervals by the IBM 1800. Although the top composition analyzer provides a continuous signal, for control purposes a sample interval of 4 s is employed. As can be seen from Figure 4, top composition is controlled by manipulating reflux flow as is common in industrial practice. The steam rate is maintained constant by means of the flow controller. Note that the control system operates in a supervisory capacity with the computer providing set points to the analog flow loops. Additional details of the column and its associated instrumentation have been described elsewhere (Svrcek, 1967; Pacey, 1973). Process Model. In order to employ the Smith predictor or analytical predictor control schemes, a mathematical model
Results Simulation. A digital computer simulation using the IBM S/360 CSMP program was conducted to evaluate the performance of the Smith predictor and analytical predictor control schemes compared to P I control. The SP algorithm given in eq 2-5 and the AP algorithm in eq 13,15, and 16 were employed using a 64-s sampling interval. The digital PI controller was tuned using an approach suggested by Moore et al. (1969) in which tuning relations for continuous controllers are employed but the process time delay, T , is replaced by an effective time delay, T’. Initially, values of K , and SI were calculated from the IAE tuning relations for load disturbances given in eq 18 and 19 using an effective time delay of T’ = T T,/2 as suggested by Moore et al. (1969). The resulting control behavior exhibited excessive oscillations. However, satisfactory control performance did result when the controller constants were calculated using an effective time delay of T’ = T T, as recommended by Mollenkamp et al. (1973). It is to be noted that this value of T‘ corresponds to the largest possible time delay due to sampling (e.g., a load disturbance occurring immediately after a sampling instant). Since the Smith predictor control scheme contains a P I controller, the same tuning relations were employed using an effective time delay of T‘ = T, rather than T’= T$2. The process time delay is not included since the Smith predictor itself compensates
+
+
Ind. Eng. Chem. Process Des. Dev., Vol. 17, No. 1, 1978
Table 11. Controller Parameters for Simulation Study Control scheme PI SP AP a
Effective time delay, T‘ T+T, T, NIA
65
“i 1.5 r
K,, g/s/%
71, s
6.31 11.6 24.15
355 230 0.21a
>-
1.0
Eg
0 0 0 0 0
PI
Numerical value of K I expressed in g/% s2. V
01’
Table 111. Comparison of Control System Performance Control scheme
IAE
PI SP AP
267 180 142
Set point change Overshoot. %
’
’
20
’
I
40 TIME (min)
60
80
Figure 6. Comparison of closed loop responses for a 1%increase in set point.
0.470 0.160 0.000
IAE PI SP AP
0
Load disturbance Max. deviation. % 0.200 0.164 0.164
86.3 139 18.2
0.05
for this time delay. 0.984 T‘ K, = -
(4
TI =
);(T’
1.6447,
-0.986
8 (18)
0.707
The analytical predictor was tuned using the deadbeat controller relations given by Moore (1969) and shown in eq 20 and 21. The calculated controller constants are summarized in Table 11.
Simulations were conducted for step changes of +1%in composition set point and for load disturbances of +2@? in feed flow rate using a sampling interval, T,, of 64 s. Typical results are shown in Figures 6 and 7 and Table 111.For the set point changes in Figure 6 both the analytical predictor and the Smith predictor exhibit superior performance compared to PI control. In fact, the analytical predictor behaves as a deadbeat controller providing better control than even the Smith predictor which exhibits a small overshoot. For the feed flow rate change in Figure 7, the analytical predictor gives a substantial improvement over the Smith predictor and the P I controller. In the case of the Smith predictor the very slow return of the composition to its set point was exhibited for a wide range of controller constants. This behavior is characteristic of Smith predictor control when load dynamics are significant, as has been shown by Meyer et al. (1976) for continuous controllers. T o establish the influence of model errors on’ the performance of the Smith and analytical predictors, simulations were performed using predictor models which contained incorrect parameter values. The controller constants were also calculated on the basis of the incorrect process models. The resulting predictor control schemes (including incorrect predictor models) were then used to control the correct process model. Figures 8 and 9 illustrate the performance of the predictor control schemes for load changes when the predictor model contains a process gain or time delay which is 20% too low. A comparison of Figures 8 and 9 indicates that the AP
0
0
20
40
60
80
TIME (,min)
Figure 7. Comparison of closed loop responses for a 20%increase in feed flow rate. control scheme is more sensitive than the S P scheme to these modeling error% The AP control system was also more sensitive when the predictor parameters were 20% too high and set point changes were considered (Meyer, 1977). However, it is quite possible that better AP responses could be obtained by tuning of the controller constants. Experimental Results An experimental evaluation of the different control schemes was performed using the IBM 1800 computer to implement the control algorithms. The tests were conducted by operating the column at the nominal steady-state operating conditions for a period of at least 0.5 h before the change in feed flow rate or composition set point was introduced. During the course of the run, the process variables were sampled every 32 s and the data were stored on disk. Initial tests were performed using controller constants calculated from eq 18-21 for the three different control schemes. However, the calculated controller constants resulted in very oscillatory experimental responses so trial and error tuning became necessary using the IAE performance criterion as a guide for selecting controller constants. (It is not claimed that optimal IAE values were established.) The large discrepancy between the calculated and tuned controller constants is obvious from the values summarized in Table IV. It should be noted that the tuned controller constants were determined on the basis of step decreases in set point. The control behavior of the column for a step decrease in set point of 1%is shown in Figure 10 for PI, Smith predictor, and analytical predictor control. The simulated responses were calculated on the basis of the process model in eq 19 using the experimental controller constants. It can be seen from Figure 10 that, in general, the simulated behavior of the column exhibits a much shorter response time than was observed experimentally. This implies that the process has higher order dynamics that are not included in the
66
Ind. Eng. Chem. Process Des. Dev., Vol. 17, No. 1, 1978
Table IV. Comparison of Calculated and Experimentally Tuned Controller Constants
SMITH PREDICTOR A A A A A Z:rcT GAIN
"16\
2 0.12
00000
- 2 0 % ERROR IN
I
-s
0.08
K,, g/s/%
PI SP AP
7.98 15.8 32.9
Z 0.04
0 I9
2
0 AAAAACORRECT TIME DELAY
0.16k
0
Controller Constants Calculated Experimental
Control scheme
a
71,
s
367 225 0.29*
K,,PIS/%
71,
4.72 10.0 10.0
367 250
s
0.06a
Value of KI in units of gl% s2.
TIME DELAY
P I CONTROL SIMULATION
" 01 0
'
--
' ' '
15
I
"
-EXPERIMENTAL '
30 45 TIME (min)
I
I
60
$
96.0
::::k,
$ 95.5
Figure 8. Effect of gain and time delay errors on SP control behav-
5 c
ior.
SP CONTROL
,
3 96.0 ANALYTICAL PRE DlCTOR
\-*-
, , , , , , ,
a
5 95.5
,
0
AP CONTROL 96.0 95'50
15
30
45
60
TIME (min)
Figure 10. Experimental comparison of PI, SP, and AP control for a 1%decrease in composition set point.
0
5
IO
15
20
25
30
TIME ( m i n )
Figure 9. Effect of gain and time delay errors on AP control behav-
0 v,
97.0
-
---.
ior.
first-order plus time delay model. The experimental results in Figure 10 demonstrate the superior control provided by both the Smith predictor and analytical predictor schemes with the Smith predictor exhibiting slightly better control. This is in contrast to the simulation results where the analytical predictor performance was superior. The results in Figure 11indicate that the Smith predictor and analytical predictor provide better regulatory control than the PI controller. These experimental and simulated responses are in good agreement but the simulated responses tend to "lead" the experimental responses, as was the case for the set point responses in Figure 10. Conclusions Two time delay compensation techniques, the analytical and Smith predictors, have been successfully used to control top composition in a pilot scale, methanol-water column. The experimental results indicate that both the analytical and Smith predictors provide a significant improvement over the P I controller for regulatory and servo control. In the experimental tests, the Smith predictor performed slightly better than the analytical predictor; however, in the simulation study the analytical predictor was superior especially for load changes where the Smith predictor resulted in very sluggish responses.
The simulation study demonstrated that both predictor control schemes exhibit a satisfactory degree of insensitivity to modeling errors. For example, satisfactory control could still be obtained when the assumed model parameters were 20%too high or too low. Larger modeling errors could probably be accommodated by tuning the controller constants in the predictor control schemes. In this application the predictor techniques provided significantly improved control even though the process time delay is relatively small (Le,, a time delayhime constant ratio of 0.06). For processes with larger time delays, at least the same degree of improvement is anticipated providing that a reasonably accurate process model is available. Nomenclature A = exp(-T,/2rp) AP = analytical predictor B = exp(-T,/rp) C,c = output (controlled variable)
Ind. Eng. Chem. Process Des. Dev., Vol. 17, No. 1, 1978 67
c' = output of system without time delay c" = output of system with time delay CD = top composition D,d = load disturbance e = controller input E = exp(-PT,/rp) F = feed flow rate G, = controller transfer function GC = gas chromatograph GL = load transfer function G = process transfer function Ip= measurement transfer function IAE = integral absolute error k = positiveinteger K , = controller gain K I = integral controller constant K , = processgain N = nonnegative integer PI = proportional-integral control R = reflux flowrate Rset,r = set point s = Laplaceoperator SP = Smith predictor T,Tl,Tz = time delays T' = effective time delay T, = sampling interval U,u = manipulated variable ZOH = zeroorder hold Greek Symbols = constant, 0 < (3 < 1 0 = dimensionless time delay p = calibrated set point TI = reset time T,, = process time constant
p
Subscript k = denotes value a t the k t h sampling instant Superscript = denotes a predicted value
Literature Cited Alevisakis, G., Seborg, D. E., Int. J. Control, 3, 541 (1973). Alevisakis, G., Seborg, D. E., Chem. Eng. Sci., 29, 373 (1974). Astrom, K. J., lnt. J. Control(in press), 1977. Buckley, P. S.,Proceedings, 1st IFAC Congress, Moscow, 1960. Doss, J. E., Ph.D. Thesis, Department of Chemical Engineering, University of Tennessee. 1974. Doss, J. E., Moore, C. F., 74th National AlChE Meeting, New Orleans, La., 1973. Eisenberg, L., /SA Trans., 6 (4), 329 (1967). Garland, E., Marshall. J. E., Electron. Lett., 10, 308 (1974). Garland, B., Marshall, J. E., lnt. J. Control, 21, 681 (1975). Gray, J. O., Hunt, P. W. B., Electron. Lett., 7 (12), 335 (1971a). Gray, J. O., Hunt, P. W. B., Electron. Lett., 7 (5/6), 131 (1971b). Lupfer, D. E., Oglesby, M. W., /SA J., 8 ( I I ) , 53 (1961). Lupfer, D. E., Oglesby, M. W., /SA Trans., 1, 72 (1962). Marshall, J. E., lnt. J. Control, 18, 933 (1974). Mee, D. H.. lnt. J. Control, 18, 1151 (1973). Meyer, C., M.Sc. Thesis, University of Alberta, Edmonton, 1977. Meyer, C., Seborg, D. E., Wood, R. K., Chem. fng. Sci., 31, 775 (1976). Meyer, C., Seborg, D. E., Wood, R. K., paper to be presented at the 70th Annual AlChE Meeting, New York, N.Y., Nov 1977. Mollenkamp, R. A,, Smith, C. L., Corripio, A. B., lnstrum. Control Syst., 46 (E), 47 (1973). Moore, C. F., Ph.D. Thesis, Department of Chemical Engineering,Louisiana State University, 1969. Moore, C. F., Smith, C. L., Murrill. P. W., lnstrum. Control Syst., 43, (1). 70 (1970). Moore, C. F., Smith, C. L., Murrill, P. W., lnstrum. fract., (I), 45 (1969). Nielsen, G., Proceedings, 4th IFAC Congress, Warsaw, 1969. Pacey, W. C., MSc. Thesis, University of Alberta, Edmonton, 1973. Prasad, C. C., Krishnaswamy, P. R., Chem. fng. Sci.. 30, 207 (1975). Rademaker, O., Rijnsdorp, J. E., Maarleveid, A., "Dynamics and Control of Continuous Distillation Units", Elsevier Scientific Publishing Co., New York, N.Y., 1975. Shinskey, F. G., "Distillation Control", McGraw-Hill, New York, N.Y., 1977. Smith, C. A., Groves, F. R., Proceedings, 28th Annual ISA Conference, Houston, Texas, 1973. Smith, 0. J. M., /SA J., 6 (2), 28 (1959). Smith, 0. J. M., Chem. Eng. frog., 53, (9,217 (1957). Svrcek, W. Y., Ph.D. Thesis, University of Alberta, Edmonton, 1967. Wood, R . K., Berry. M. W., Chem. Eng. Sci., 28, 1707 (1973).
Received for reuiew February 1,1977 Accepted July 29,1977 Financial support was provided by the National Research Council of Canada through research grants A5827 and A1944. A preliminary version of this paper was presented a t the 5th IFAC/IFIP Conference on Digital Computer Applications to Process Control, The Hague, Netherlands, June 1977.
Decontamination of Alkaline Radioactive Waste by Ion Exchange J. R. Wiley Savannah River Laboratory, E. 1. du font de Nemours 8 Co., Aiken, South Carolina 2980 1
An ion-exchange process was developed to remove cesium-137, strontium-90, and plutonium from alkaline salt solutions. About 20 million gal of alkaline salt cake and supernatant solution from processing nuclear fuels and materials for defense programs are presently stored at the Savannah River Plant. Ion exchange may be used to decontaminate this radioactive waste during a proposed waste solidification program. In development tests, 100-L quantities of liquid waste were decontaminated. Decontamination factors were 4 X lo5 for 13'Cs, 5 X l o 3 for 90Sr, and 300 for Pu. The separated radionuclides were concentrated by a factor of 1500 and immobilized by adsorption onto zeolite. Residual 137Cs, and Pu activity in the decontaminated product was about 6 nCi/ g. loSRu,the most hazardous radionuclide remaining after 137Csremoval, will decay to 6 nCi/g about 10 years after the waste is processed.
Introduction Aqueous radioactive waste is produced from nuclear fuel reprocessing and associated operations for producing nuclear materials for defense programs a t the Savannah River Plant (SRP). This waste is neutralized with NaOH and stored un0019-7882/78/1117-0067$01.00/0
derground in large carbon steel tanks. After addition of NaOH, many elements (such as Fe, Mn, Ca, and Hg) precipitate from the solution as hydroxides or hydrated oxides to form a sludge layer on the bottom of the waste tanks. Most waste actinides and WSr are in this insoluble sludge. NaN03, NaN02, NaA102, 0 1978 American Chemical Society
